An architecture-independent workload characterization model for parallel computer architectures

A model is presented for representing and comparing workloads, based on the way they would exercise parallel machines. This workload characterization is derived from parallel instruction centroid and parallel workload similarity. The centroid is a simple measure that aggregates average parallelism instruction mix, and critical path length. When captured with abstracted information about communication requirements, the result is a powerful tool in understanding the requirements of workloads and their potential performance on target machines. The workload similarity is based on measuring the normalized Euclidean distance (ned) between workload centroids. It is shown that this workload representation method outperforms comparable ones in accuracy, as well as in time and space requirements. Analysis of the NAS Parallel Benchmarks and their performance is presented to demonstrate some of the applications, such as performance prediction with good accuracy, and insight provided by this model.